Overview

Dataset statistics

Number of variables20
Number of observations10645
Missing cells106397
Missing cells (%)50.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.8 MiB
Average record size in memory574.4 B

Variable types

Categorical4
Text8
Numeric7
Unsupported1

Alerts

Unnamed: 10 has 10645 (100.0%) missing valuesMissing
Unnamed: 11 has 10641 (> 99.9%) missing valuesMissing
Unnamed: 12 has 10632 (99.9%) missing valuesMissing
Unnamed: 13 has 10640 (> 99.9%) missing valuesMissing
Unnamed: 14 has 10640 (> 99.9%) missing valuesMissing
Unnamed: 15 has 10640 (> 99.9%) missing valuesMissing
Unnamed: 16 has 10639 (99.9%) missing valuesMissing
Unnamed: 17 has 10640 (> 99.9%) missing valuesMissing
Unnamed: 18 has 10640 (> 99.9%) missing valuesMissing
Unnamed: 19 has 10640 (> 99.9%) missing valuesMissing
LCOM is highly skewed (γ1 = 32.72883226)Skewed
Unnamed: 10 is an unsupported type, check if it needs cleaning or further analysisUnsupported
WMC has 147 (1.4%) zerosZeros
DIT has 147 (1.4%) zerosZeros
NOC has 9740 (91.5%) zerosZeros
CBO has 145 (1.4%) zerosZeros
LCOM has 4047 (38.0%) zerosZeros
MAXCC has 566 (5.3%) zerosZeros
AVGCC has 566 (5.3%) zerosZeros

Reproduction

Analysis started2024-02-06 04:17:20.706252
Analysis finished2024-02-06 04:17:27.850935
Duration7.14 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Project
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size638.1 KiB
jEdit
3695 
camel
3575 
ant
2442 
ivy
933 

Length

Max length5
Median length5
Mean length4.3658995
Min length3

Characters and Unicode

Total characters46475
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowjEdit
2nd rowjEdit
3rd rowjEdit
4th rowjEdit
5th rowjEdit

Common Values

ValueCountFrequency (%)
jEdit 3695
34.7%
camel 3575
33.6%
ant 2442
22.9%
ivy 933
 
8.8%

Length

2024-02-06T04:17:27.980349image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-06T04:17:28.169019image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
jedit 3695
34.7%
camel 3575
33.6%
ant 2442
22.9%
ivy 933
 
8.8%

Most occurring characters

ValueCountFrequency (%)
t 6137
13.2%
a 6017
12.9%
i 4628
10.0%
j 3695
8.0%
E 3695
8.0%
d 3695
8.0%
c 3575
7.7%
m 3575
7.7%
e 3575
7.7%
l 3575
7.7%
Other values (3) 4308
9.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 42780
92.0%
Uppercase Letter 3695
 
8.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 6137
14.3%
a 6017
14.1%
i 4628
10.8%
j 3695
8.6%
d 3695
8.6%
c 3575
8.4%
m 3575
8.4%
e 3575
8.4%
l 3575
8.4%
n 2442
 
5.7%
Other values (2) 1866
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
E 3695
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 46475
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 6137
13.2%
a 6017
12.9%
i 4628
10.0%
j 3695
8.0%
E 3695
8.0%
d 3695
8.0%
c 3575
7.7%
m 3575
7.7%
e 3575
7.7%
l 3575
7.7%
Other values (3) 4308
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 6137
13.2%
a 6017
12.9%
i 4628
10.0%
j 3695
8.0%
E 3695
8.0%
d 3695
8.0%
c 3575
7.7%
m 3575
7.7%
e 3575
7.7%
l 3575
7.7%
Other values (3) 4308
9.3%

Version
Categorical

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size621.9 KiB
1.6
1775 
1.4
1708 
4.3
1132 
1.7
1066 
4.2
805 
Other values (9)
4159 

Length

Max length5
Median length3
Mean length2.8100517
Min length1

Characters and Unicode

Total characters29913
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.2
2nd row4.3
3rd row4.1
4th row4
5th row4.1

Common Values

ValueCountFrequency (%)
1.6 1775
16.7%
1.4 1708
16.0%
4.3 1132
10.6%
1.7 1066
10.0%
4.2 805
7.6%
1.2 765
7.2%
4.1 644
 
6.0%
4 606
 
5.7%
3.2.1 508
 
4.8%
2 477
 
4.5%
Other values (4) 1159
10.9%

Length

2024-02-06T04:17:28.356928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1.6 1775
16.7%
1.4 1708
16.0%
4.3 1132
10.6%
1.7 1066
10.0%
4.2 805
7.6%
1.2 765
7.2%
4.1 644
 
6.0%
4 606
 
5.7%
3.2.1 508
 
4.8%
2 477
 
4.5%
Other values (4) 1159
10.9%

Most occurring characters

ValueCountFrequency (%)
. 9634
32.2%
1 7760
25.9%
4 4895
16.4%
2 2555
 
8.5%
3 1827
 
6.1%
6 1775
 
5.9%
7 1066
 
3.6%
5 401
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20279
67.8%
Other Punctuation 9634
32.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7760
38.3%
4 4895
24.1%
2 2555
 
12.6%
3 1827
 
9.0%
6 1775
 
8.8%
7 1066
 
5.3%
5 401
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 9634
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29913
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9634
32.2%
1 7760
25.9%
4 4895
16.4%
2 2555
 
8.5%
3 1827
 
6.1%
6 1775
 
5.9%
7 1066
 
3.6%
5 401
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29913
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9634
32.2%
1 7760
25.9%
4 4895
16.4%
2 2555
 
8.5%
3 1827
 
6.1%
6 1775
 
5.9%
7 1066
 
3.6%
5 401
 
1.3%

Class
Text

Distinct4879
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
2024-02-06T04:17:28.594799image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length107
Median length84
Mean length44.044246
Min length7

Characters and Unicode

Total characters468851
Distinct characters66
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2283 ?
Unique (%)21.4%

Sample

1st roworg.gjt.sp.jedit.gui.KeyEventWorkaround
2nd roworg.gjt.sp.jedit.gui.KeyEventWorkaround
3rd roworg.gjt.sp.jedit.gui.KeyEventWorkaround
4th roworg.gjt.sp.jedit.gui.KeyEventWorkaround
5th roworg.gjt.sp.jedit.pluginmgr.PluginManager$ActionHandler
ValueCountFrequency (%)
org.gjt.sp.jedit.gui.keyeventworkaround 5
 
< 0.1%
org.gjt.sp.jedit.textutilities 5
 
< 0.1%
org.apache.tools.ant.types.enumeratedattribute 5
 
< 0.1%
org.apache.tools.ant.taskdefs.generatekey$distinguishedname 5
 
< 0.1%
org.apache.tools.ant.taskdefs.filter 5
 
< 0.1%
org.gjt.sp.jedit.search.boyermooresearchmatcher 5
 
< 0.1%
org.gjt.sp.jedit.gui.enhanceddialog$containerhandler 5
 
< 0.1%
org.gjt.sp.jedit.gui.jcheckboxlist 5
 
< 0.1%
org.gjt.sp.jedit.gui.ioprogressmonitor$threadprogress 5
 
< 0.1%
org.apache.tools.ant.taskdefs.compilers.defaultcompileradapter 5
 
< 0.1%
Other values (4868) 10595
99.5%
2024-02-06T04:17:29.127921image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 49950
 
10.7%
e 44590
 
9.5%
a 36330
 
7.7%
o 34514
 
7.4%
t 31984
 
6.8%
r 30321
 
6.5%
s 21117
 
4.5%
n 20237
 
4.3%
p 19753
 
4.2%
c 19345
 
4.1%
Other values (56) 160710
34.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 380537
81.2%
Other Punctuation 49950
 
10.7%
Uppercase Letter 32698
 
7.0%
Currency Symbol 3894
 
0.8%
Decimal Number 1740
 
0.4%
Dash Punctuation 20
 
< 0.1%
Connector Punctuation 12
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 44590
11.7%
a 36330
 
9.5%
o 34514
 
9.1%
t 31984
 
8.4%
r 30321
 
8.0%
s 21117
 
5.5%
n 20237
 
5.3%
p 19753
 
5.2%
c 19345
 
5.1%
i 19139
 
5.0%
Other values (16) 103207
27.1%
Uppercase Letter
ValueCountFrequency (%)
C 2968
 
9.1%
S 2950
 
9.0%
P 2713
 
8.3%
E 2272
 
6.9%
M 2204
 
6.7%
R 2065
 
6.3%
B 1985
 
6.1%
H 1922
 
5.9%
T 1780
 
5.4%
D 1585
 
4.8%
Other values (16) 10254
31.4%
Decimal Number
ValueCountFrequency (%)
1 880
50.6%
2 371
21.3%
4 145
 
8.3%
3 133
 
7.6%
5 52
 
3.0%
7 41
 
2.4%
6 40
 
2.3%
8 29
 
1.7%
0 25
 
1.4%
9 24
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 49950
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 3894
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 413235
88.1%
Common 55616
 
11.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 44590
 
10.8%
a 36330
 
8.8%
o 34514
 
8.4%
t 31984
 
7.7%
r 30321
 
7.3%
s 21117
 
5.1%
n 20237
 
4.9%
p 19753
 
4.8%
c 19345
 
4.7%
i 19139
 
4.6%
Other values (42) 135905
32.9%
Common
ValueCountFrequency (%)
. 49950
89.8%
$ 3894
 
7.0%
1 880
 
1.6%
2 371
 
0.7%
4 145
 
0.3%
3 133
 
0.2%
5 52
 
0.1%
7 41
 
0.1%
6 40
 
0.1%
8 29
 
0.1%
Other values (4) 81
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 468851
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 49950
 
10.7%
e 44590
 
9.5%
a 36330
 
7.7%
o 34514
 
7.4%
t 31984
 
6.8%
r 30321
 
6.5%
s 21117
 
4.5%
n 20237
 
4.3%
p 19753
 
4.2%
c 19345
 
4.1%
Other values (56) 160710
34.3%

WMC
Real number (ℝ)

ZEROS 

Distinct111
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8892438
Minimum0
Maximum413
Zeros147
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size83.3 KiB
2024-02-06T04:17:29.285458image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q39
95-th percentile25
Maximum413
Range413
Interquartile range (IQR)7

Descriptive statistics

Standard deviation14.631957
Coefficient of variation (CV)1.8546717
Kurtosis259.58351
Mean7.8892438
Median Absolute Deviation (MAD)2
Skewness12.487749
Sum83981
Variance214.09418
MonotonicityNot monotonic
2024-02-06T04:17:29.387004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 2484
23.3%
3 1528
14.4%
4 953
 
9.0%
5 766
 
7.2%
1 648
 
6.1%
6 571
 
5.4%
7 459
 
4.3%
8 378
 
3.6%
9 301
 
2.8%
10 256
 
2.4%
Other values (101) 2301
21.6%
ValueCountFrequency (%)
0 147
 
1.4%
1 648
 
6.1%
2 2484
23.3%
3 1528
14.4%
4 953
 
9.0%
5 766
 
7.2%
6 571
 
5.4%
7 459
 
4.3%
8 378
 
3.6%
9 301
 
2.8%
ValueCountFrequency (%)
413 1
< 0.1%
407 1
< 0.1%
399 1
< 0.1%
351 2
< 0.1%
262 1
< 0.1%
259 1
< 0.1%
248 1
< 0.1%
239 1
< 0.1%
211 1
< 0.1%
205 1
< 0.1%

DIT
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0249883
Minimum0
Maximum8
Zeros147
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size83.3 KiB
2024-02-06T04:17:29.459294image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q33
95-th percentile5
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4769732
Coefficient of variation (CV)0.72937373
Kurtosis1.9114953
Mean2.0249883
Median Absolute Deviation (MAD)0
Skewness1.5441357
Sum21556
Variance2.1814499
MonotonicityNot monotonic
2024-02-06T04:17:29.539545image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 5412
50.8%
2 2249
21.1%
3 1320
 
12.4%
4 573
 
5.4%
5 446
 
4.2%
6 346
 
3.3%
0 147
 
1.4%
7 128
 
1.2%
8 24
 
0.2%
ValueCountFrequency (%)
0 147
 
1.4%
1 5412
50.8%
2 2249
21.1%
3 1320
 
12.4%
4 573
 
5.4%
5 446
 
4.2%
6 346
 
3.3%
7 128
 
1.2%
8 24
 
0.2%
ValueCountFrequency (%)
8 24
 
0.2%
7 128
 
1.2%
6 346
 
3.3%
5 446
 
4.2%
4 573
 
5.4%
3 1320
 
12.4%
2 2249
21.1%
1 5412
50.8%
0 147
 
1.4%

NOC
Real number (ℝ)

ZEROS 

Distinct40
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36618131
Minimum0
Maximum102
Zeros9740
Zeros (%)91.5%
Negative0
Negative (%)0.0%
Memory size83.3 KiB
2024-02-06T04:17:29.615546image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum102
Range102
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.4294755
Coefficient of variation (CV)6.6346246
Kurtosis440.66942
Mean0.36618131
Median Absolute Deviation (MAD)0
Skewness16.62247
Sum3898
Variance5.9023511
MonotonicityNot monotonic
2024-02-06T04:17:29.688563image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 9740
91.5%
1 318
 
3.0%
2 245
 
2.3%
3 97
 
0.9%
4 45
 
0.4%
5 38
 
0.4%
6 28
 
0.3%
7 15
 
0.1%
8 13
 
0.1%
13 13
 
0.1%
Other values (30) 93
 
0.9%
ValueCountFrequency (%)
0 9740
91.5%
1 318
 
3.0%
2 245
 
2.3%
3 97
 
0.9%
4 45
 
0.4%
5 38
 
0.4%
6 28
 
0.3%
7 15
 
0.1%
8 13
 
0.1%
9 9
 
0.1%
ValueCountFrequency (%)
102 1
< 0.1%
59 2
< 0.1%
52 1
< 0.1%
40 1
< 0.1%
39 1
< 0.1%
38 2
< 0.1%
36 2
< 0.1%
35 2
< 0.1%
34 1
< 0.1%
33 1
< 0.1%

CBO
Real number (ℝ)

ZEROS 

Distinct139
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8911226
Minimum0
Maximum499
Zeros145
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size83.3 KiB
2024-02-06T04:17:29.788763image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q310
95-th percentile25
Maximum499
Range499
Interquartile range (IQR)7

Descriptive statistics

Standard deviation17.094062
Coefficient of variation (CV)1.9225988
Kurtosis207.99929
Mean8.8911226
Median Absolute Deviation (MAD)3
Skewness11.441708
Sum94646
Variance292.20695
MonotonicityNot monotonic
2024-02-06T04:17:29.925590image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1275
12.0%
4 1252
11.8%
3 1200
11.3%
5 936
 
8.8%
1 862
 
8.1%
6 722
 
6.8%
7 621
 
5.8%
8 522
 
4.9%
9 438
 
4.1%
10 346
 
3.3%
Other values (129) 2471
23.2%
ValueCountFrequency (%)
0 145
 
1.4%
1 862
8.1%
2 1275
12.0%
3 1200
11.3%
4 1252
11.8%
5 936
8.8%
6 722
6.8%
7 621
5.8%
8 522
4.9%
9 438
 
4.1%
ValueCountFrequency (%)
499 1
< 0.1%
448 1
< 0.1%
389 1
< 0.1%
365 1
< 0.1%
346 1
< 0.1%
274 1
< 0.1%
272 1
< 0.1%
262 1
< 0.1%
258 1
< 0.1%
243 1
< 0.1%

LCOM
Real number (ℝ)

SKEWED  ZEROS 

Distinct547
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.886895
Minimum0
Maximum41713
Zeros4047
Zeros (%)38.0%
Negative0
Negative (%)0.0%
Memory size83.3 KiB
2024-02-06T04:17:30.084035image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q315
95-th percentile208.8
Maximum41713
Range41713
Interquartile range (IQR)15

Descriptive statistics

Standard deviation813.56882
Coefficient of variation (CV)10.184009
Kurtosis1427.3065
Mean79.886895
Median Absolute Deviation (MAD)1
Skewness32.728832
Sum850396
Variance661894.23
MonotonicityNot monotonic
2024-02-06T04:17:30.219341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4047
38.0%
1 1493
 
14.0%
3 724
 
6.8%
6 374
 
3.5%
4 351
 
3.3%
10 183
 
1.7%
15 168
 
1.6%
2 161
 
1.5%
8 125
 
1.2%
9 123
 
1.2%
Other values (537) 2896
27.2%
ValueCountFrequency (%)
0 4047
38.0%
1 1493
 
14.0%
2 161
 
1.5%
3 724
 
6.8%
4 351
 
3.3%
5 65
 
0.6%
6 374
 
3.5%
7 75
 
0.7%
8 125
 
1.2%
9 123
 
1.2%
ValueCountFrequency (%)
41713 2
< 0.1%
20326 1
< 0.1%
19801 1
< 0.1%
16973 1
< 0.1%
16336 1
< 0.1%
13617 1
< 0.1%
13445 1
< 0.1%
12328 1
< 0.1%
11794 1
< 0.1%
11469 1
< 0.1%

MAXCC
Real number (ℝ)

ZEROS 

Distinct59
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5210897
Minimum0
Maximum167
Zeros566
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size83.3 KiB
2024-02-06T04:17:30.330907image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile12
Maximum167
Range167
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.1151923
Coefficient of variation (CV)1.7367329
Kurtosis208.7377
Mean3.5210897
Median Absolute Deviation (MAD)1
Skewness10.209553
Sum37482
Variance37.395576
MonotonicityNot monotonic
2024-02-06T04:17:30.418081image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4754
44.7%
2 1390
 
13.1%
3 985
 
9.3%
4 658
 
6.2%
0 566
 
5.3%
5 485
 
4.6%
6 362
 
3.4%
7 260
 
2.4%
8 213
 
2.0%
9 147
 
1.4%
Other values (49) 825
 
7.8%
ValueCountFrequency (%)
0 566
 
5.3%
1 4754
44.7%
2 1390
 
13.1%
3 985
 
9.3%
4 658
 
6.2%
5 485
 
4.6%
6 362
 
3.4%
7 260
 
2.4%
8 213
 
2.0%
9 147
 
1.4%
ValueCountFrequency (%)
167 3
< 0.1%
163 1
 
< 0.1%
85 2
< 0.1%
84 1
 
< 0.1%
71 1
 
< 0.1%
68 1
 
< 0.1%
62 1
 
< 0.1%
61 2
< 0.1%
60 1
 
< 0.1%
57 1
 
< 0.1%

AVGCC
Real number (ℝ)

ZEROS 

Distinct773
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2655909
Minimum0
Maximum28.666666
Zeros566
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size83.3 KiB
2024-02-06T04:17:30.518135image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.6666667
median1
Q31.5
95-th percentile3.3333333
Maximum28.666666
Range28.666666
Interquartile range (IQR)0.8333333

Descriptive statistics

Standard deviation1.2169636
Coefficient of variation (CV)0.96157741
Kurtosis61.841359
Mean1.2655909
Median Absolute Deviation (MAD)0.4
Skewness5.1357307
Sum13472.215
Variance1.4810004
MonotonicityDecreasing
2024-02-06T04:17:30.631720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1760
16.5%
0.5 1585
 
14.9%
0.6666667 808
 
7.6%
0 566
 
5.3%
0.75 360
 
3.4%
1.5 300
 
2.8%
0.8 288
 
2.7%
2 282
 
2.6%
0.8333333 206
 
1.9%
1.3333334 172
 
1.6%
Other values (763) 4318
40.6%
ValueCountFrequency (%)
0 566
 
5.3%
0.2 5
 
< 0.1%
0.25 8
 
0.1%
0.33333334 139
 
1.3%
0.375 5
 
< 0.1%
0.4 13
 
0.1%
0.42857143 2
 
< 0.1%
0.44444445 1
 
< 0.1%
0.5 1585
14.9%
0.53333336 1
 
< 0.1%
ValueCountFrequency (%)
28.666666 1
< 0.1%
25.142857 1
< 0.1%
20.5 1
< 0.1%
20 1
< 0.1%
15 1
< 0.1%
14.5 2
< 0.1%
12.5 1
< 0.1%
12 1
< 0.1%
11.1 1
< 0.1%
11 1
< 0.1%

Unnamed: 10
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10645
Missing (%)100.0%
Memory size83.3 KiB

Unnamed: 11
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing10641
Missing (%)> 99.9%
Memory size332.9 KiB
2024-02-06T04:17:30.722443image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4
Min length3

Characters and Unicode

Total characters16
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st rowjEdit
2nd rowcamel
3rd rowivy
4th rowant
ValueCountFrequency (%)
jedit 1
25.0%
camel 1
25.0%
ivy 1
25.0%
ant 1
25.0%
2024-02-06T04:17:30.926267image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 2
12.5%
t 2
12.5%
a 2
12.5%
j 1
 
6.2%
E 1
 
6.2%
d 1
 
6.2%
c 1
 
6.2%
m 1
 
6.2%
e 1
 
6.2%
l 1
 
6.2%
Other values (3) 3
18.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15
93.8%
Uppercase Letter 1
 
6.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 2
13.3%
t 2
13.3%
a 2
13.3%
j 1
6.7%
d 1
6.7%
c 1
6.7%
m 1
6.7%
e 1
6.7%
l 1
6.7%
v 1
6.7%
Other values (2) 2
13.3%
Uppercase Letter
ValueCountFrequency (%)
E 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 2
12.5%
t 2
12.5%
a 2
12.5%
j 1
 
6.2%
E 1
 
6.2%
d 1
 
6.2%
c 1
 
6.2%
m 1
 
6.2%
e 1
 
6.2%
l 1
 
6.2%
Other values (3) 3
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 2
12.5%
t 2
12.5%
a 2
12.5%
j 1
 
6.2%
E 1
 
6.2%
d 1
 
6.2%
c 1
 
6.2%
m 1
 
6.2%
e 1
 
6.2%
l 1
 
6.2%
Other values (3) 3
18.8%

Unnamed: 12
Text

MISSING 

Distinct13
Distinct (%)100.0%
Missing10632
Missing (%)99.9%
Memory size333.2 KiB
2024-02-06T04:17:31.031045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length11
Median length7
Mean length6.5384615
Min length1

Characters and Unicode

Total characters85
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)100.0%

Sample

1st rowMAXCC
2nd rowAverage
3rd row4.88173207
4th row2.022377622
5th row3.015005359
ValueCountFrequency (%)
maxcc 1
 
7.7%
average 1
 
7.7%
4.88173207 1
 
7.7%
2.022377622 1
 
7.7%
3.015005359 1
 
7.7%
3.84971335 1
 
7.7%
div/0 1
 
7.7%
2 1
 
7.7%
project 1
 
7.7%
jedit 1
 
7.7%
Other values (3) 3
23.1%
2024-02-06T04:17:31.319120image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 7
 
8.2%
2 7
 
8.2%
0 6
 
7.1%
7 5
 
5.9%
e 4
 
4.7%
5 4
 
4.7%
. 4
 
4.7%
t 3
 
3.5%
a 3
 
3.5%
8 3
 
3.5%
Other values (28) 39
45.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40
47.1%
Lowercase Letter 27
31.8%
Uppercase Letter 11
 
12.9%
Other Punctuation 7
 
8.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4
14.8%
t 3
11.1%
a 3
11.1%
c 2
 
7.4%
i 2
 
7.4%
r 2
 
7.4%
v 2
 
7.4%
j 2
 
7.4%
o 1
 
3.7%
m 1
 
3.7%
Other values (5) 5
18.5%
Decimal Number
ValueCountFrequency (%)
3 7
17.5%
2 7
17.5%
0 6
15.0%
7 5
12.5%
5 4
10.0%
8 3
7.5%
1 3
7.5%
9 2
 
5.0%
4 2
 
5.0%
6 1
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
A 2
18.2%
C 2
18.2%
E 1
9.1%
M 1
9.1%
P 1
9.1%
V 1
9.1%
I 1
9.1%
D 1
9.1%
X 1
9.1%
Other Punctuation
ValueCountFrequency (%)
. 4
57.1%
! 1
 
14.3%
/ 1
 
14.3%
# 1
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common 47
55.3%
Latin 38
44.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4
 
10.5%
t 3
 
7.9%
a 3
 
7.9%
c 2
 
5.3%
i 2
 
5.3%
A 2
 
5.3%
r 2
 
5.3%
v 2
 
5.3%
C 2
 
5.3%
j 2
 
5.3%
Other values (14) 14
36.8%
Common
ValueCountFrequency (%)
3 7
14.9%
2 7
14.9%
0 6
12.8%
7 5
10.6%
5 4
8.5%
. 4
8.5%
8 3
6.4%
1 3
6.4%
9 2
 
4.3%
4 2
 
4.3%
Other values (4) 4
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 85
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 7
 
8.2%
2 7
 
8.2%
0 6
 
7.1%
7 5
 
5.9%
e 4
 
4.7%
5 4
 
4.7%
. 4
 
4.7%
t 3
 
3.5%
a 3
 
3.5%
8 3
 
3.5%
Other values (28) 39
45.9%

Unnamed: 13
Text

MISSING 

Distinct3
Distinct (%)60.0%
Missing10640
Missing (%)> 99.9%
Memory size332.9 KiB
2024-02-06T04:17:31.453288image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length6
Median length1
Mean length2
Min length1

Characters and Unicode

Total characters10
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)20.0%

Sample

1st rowMedian
2nd row2
3rd row1
4th row1
5th row2
ValueCountFrequency (%)
2 2
40.0%
1 2
40.0%
median 1
20.0%
2024-02-06T04:17:31.641054image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 2
20.0%
1 2
20.0%
M 1
10.0%
e 1
10.0%
d 1
10.0%
i 1
10.0%
a 1
10.0%
n 1
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5
50.0%
Decimal Number 4
40.0%
Uppercase Letter 1
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1
20.0%
d 1
20.0%
i 1
20.0%
a 1
20.0%
n 1
20.0%
Decimal Number
ValueCountFrequency (%)
2 2
50.0%
1 2
50.0%
Uppercase Letter
ValueCountFrequency (%)
M 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6
60.0%
Common 4
40.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 1
16.7%
e 1
16.7%
d 1
16.7%
i 1
16.7%
a 1
16.7%
n 1
16.7%
Common
ValueCountFrequency (%)
2 2
50.0%
1 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2
20.0%
1 2
20.0%
M 1
10.0%
e 1
10.0%
d 1
10.0%
i 1
10.0%
a 1
10.0%
n 1
10.0%

Unnamed: 14
Text

MISSING 

Distinct5
Distinct (%)100.0%
Missing10640
Missing (%)> 99.9%
Memory size332.9 KiB
2024-02-06T04:17:31.719300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length7
Median length2
Mean length3.2
Min length2

Characters and Unicode

Total characters16
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st rowMaximum
2nd row167
3rd row33
4th row29
5th row53
ValueCountFrequency (%)
maximum 1
20.0%
167 1
20.0%
33 1
20.0%
29 1
20.0%
53 1
20.0%
2024-02-06T04:17:31.898952image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 3
18.8%
m 2
12.5%
M 1
 
6.2%
a 1
 
6.2%
x 1
 
6.2%
i 1
 
6.2%
u 1
 
6.2%
1 1
 
6.2%
6 1
 
6.2%
7 1
 
6.2%
Other values (3) 3
18.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9
56.2%
Lowercase Letter 6
37.5%
Uppercase Letter 1
 
6.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 3
33.3%
1 1
 
11.1%
6 1
 
11.1%
7 1
 
11.1%
2 1
 
11.1%
9 1
 
11.1%
5 1
 
11.1%
Lowercase Letter
ValueCountFrequency (%)
m 2
33.3%
a 1
16.7%
x 1
16.7%
i 1
16.7%
u 1
16.7%
Uppercase Letter
ValueCountFrequency (%)
M 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9
56.2%
Latin 7
43.8%

Most frequent character per script

Common
ValueCountFrequency (%)
3 3
33.3%
1 1
 
11.1%
6 1
 
11.1%
7 1
 
11.1%
2 1
 
11.1%
9 1
 
11.1%
5 1
 
11.1%
Latin
ValueCountFrequency (%)
m 2
28.6%
M 1
14.3%
a 1
14.3%
x 1
14.3%
i 1
14.3%
u 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 3
18.8%
m 2
12.5%
M 1
 
6.2%
a 1
 
6.2%
x 1
 
6.2%
i 1
 
6.2%
u 1
 
6.2%
1 1
 
6.2%
6 1
 
6.2%
7 1
 
6.2%
Other values (3) 3
18.8%

Unnamed: 15
Categorical

MISSING 

Distinct2
Distinct (%)40.0%
Missing10640
Missing (%)> 99.9%
Memory size665.4 KiB
0
Minimum

Length

Max length7
Median length1
Mean length2.2
Min length1

Characters and Unicode

Total characters11
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)20.0%

Sample

1st rowMinimum
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4
 
< 0.1%
Minimum 1
 
< 0.1%
(Missing) 10640
> 99.9%

Length

2024-02-06T04:17:31.994900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-06T04:17:32.059508image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4
80.0%
minimum 1
 
20.0%

Most occurring characters

ValueCountFrequency (%)
0 4
36.4%
i 2
18.2%
m 2
18.2%
M 1
 
9.1%
n 1
 
9.1%
u 1
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6
54.5%
Decimal Number 4
36.4%
Uppercase Letter 1
 
9.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 2
33.3%
m 2
33.3%
n 1
16.7%
u 1
16.7%
Decimal Number
ValueCountFrequency (%)
0 4
100.0%
Uppercase Letter
ValueCountFrequency (%)
M 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7
63.6%
Common 4
36.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 2
28.6%
m 2
28.6%
M 1
14.3%
n 1
14.3%
u 1
14.3%
Common
ValueCountFrequency (%)
0 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4
36.4%
i 2
18.2%
m 2
18.2%
M 1
 
9.1%
n 1
 
9.1%
u 1
 
9.1%

Unnamed: 16
Text

MISSING 

Distinct6
Distinct (%)100.0%
Missing10639
Missing (%)99.9%
Memory size333.0 KiB
2024-02-06T04:17:32.125920image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length9.3333333
Min length5

Characters and Unicode

Total characters56
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)100.0%

Sample

1st rowAVGCC
2nd rowAverage
3rd row1.650023515
4th row0.915158943
5th row1.178112428
ValueCountFrequency (%)
avgcc 1
16.7%
average 1
16.7%
1.650023515 1
16.7%
0.915158943 1
16.7%
1.178112428 1
16.7%
1.230346485 1
16.7%
2024-02-06T04:17:32.538625image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 9
16.1%
5 6
10.7%
. 4
 
7.1%
4 4
 
7.1%
8 4
 
7.1%
3 4
 
7.1%
2 4
 
7.1%
0 4
 
7.1%
6 2
 
3.6%
9 2
 
3.6%
Other values (10) 13
23.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40
71.4%
Uppercase Letter 6
 
10.7%
Lowercase Letter 6
 
10.7%
Other Punctuation 4
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9
22.5%
5 6
15.0%
4 4
10.0%
8 4
10.0%
3 4
10.0%
2 4
10.0%
0 4
10.0%
6 2
 
5.0%
9 2
 
5.0%
7 1
 
2.5%
Lowercase Letter
ValueCountFrequency (%)
e 2
33.3%
g 1
16.7%
a 1
16.7%
r 1
16.7%
v 1
16.7%
Uppercase Letter
ValueCountFrequency (%)
A 2
33.3%
C 2
33.3%
V 1
16.7%
G 1
16.7%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44
78.6%
Latin 12
 
21.4%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9
20.5%
5 6
13.6%
. 4
9.1%
4 4
9.1%
8 4
9.1%
3 4
9.1%
2 4
9.1%
0 4
9.1%
6 2
 
4.5%
9 2
 
4.5%
Latin
ValueCountFrequency (%)
A 2
16.7%
e 2
16.7%
C 2
16.7%
V 1
8.3%
g 1
8.3%
a 1
8.3%
r 1
8.3%
v 1
8.3%
G 1
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9
16.1%
5 6
10.7%
. 4
 
7.1%
4 4
 
7.1%
8 4
 
7.1%
3 4
 
7.1%
2 4
 
7.1%
0 4
 
7.1%
6 2
 
3.6%
9 2
 
3.6%
Other values (10) 13
23.2%

Unnamed: 17
Text

MISSING 

Distinct3
Distinct (%)60.0%
Missing10640
Missing (%)> 99.9%
Memory size332.9 KiB
2024-02-06T04:17:32.652567image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length1
Mean length3.8
Min length1

Characters and Unicode

Total characters19
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)40.0%

Sample

1st rowMedian
2nd row1
3rd row0.85714287
4th row1
5th row1
ValueCountFrequency (%)
1 3
60.0%
median 1
 
20.0%
0.85714287 1
 
20.0%
2024-02-06T04:17:32.858663image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4
21.1%
8 2
10.5%
7 2
10.5%
M 1
 
5.3%
e 1
 
5.3%
d 1
 
5.3%
i 1
 
5.3%
a 1
 
5.3%
n 1
 
5.3%
0 1
 
5.3%
Other values (4) 4
21.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12
63.2%
Lowercase Letter 5
26.3%
Uppercase Letter 1
 
5.3%
Other Punctuation 1
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4
33.3%
8 2
16.7%
7 2
16.7%
0 1
 
8.3%
5 1
 
8.3%
4 1
 
8.3%
2 1
 
8.3%
Lowercase Letter
ValueCountFrequency (%)
e 1
20.0%
d 1
20.0%
i 1
20.0%
a 1
20.0%
n 1
20.0%
Uppercase Letter
ValueCountFrequency (%)
M 1
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13
68.4%
Latin 6
31.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4
30.8%
8 2
15.4%
7 2
15.4%
0 1
 
7.7%
. 1
 
7.7%
5 1
 
7.7%
4 1
 
7.7%
2 1
 
7.7%
Latin
ValueCountFrequency (%)
M 1
16.7%
e 1
16.7%
d 1
16.7%
i 1
16.7%
a 1
16.7%
n 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4
21.1%
8 2
10.5%
7 2
10.5%
M 1
 
5.3%
e 1
 
5.3%
d 1
 
5.3%
i 1
 
5.3%
a 1
 
5.3%
n 1
 
5.3%
0 1
 
5.3%
Other values (4) 4
21.1%

Unnamed: 18
Text

MISSING 

Distinct5
Distinct (%)100.0%
Missing10640
Missing (%)> 99.9%
Memory size332.9 KiB
2024-02-06T04:17:32.944295image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length5.6
Min length1

Characters and Unicode

Total characters28
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st rowMaximum
2nd row28.666666
3rd row9.5
4th row8
5th row8.454545
ValueCountFrequency (%)
maximum 1
20.0%
28.666666 1
20.0%
9.5 1
20.0%
8 1
20.0%
8.454545 1
20.0%
2024-02-06T04:17:33.132293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 6
21.4%
5 4
14.3%
8 3
10.7%
. 3
10.7%
4 3
10.7%
m 2
 
7.1%
M 1
 
3.6%
a 1
 
3.6%
x 1
 
3.6%
i 1
 
3.6%
Other values (3) 3
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18
64.3%
Lowercase Letter 6
 
21.4%
Other Punctuation 3
 
10.7%
Uppercase Letter 1
 
3.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 6
33.3%
5 4
22.2%
8 3
16.7%
4 3
16.7%
2 1
 
5.6%
9 1
 
5.6%
Lowercase Letter
ValueCountFrequency (%)
m 2
33.3%
a 1
16.7%
x 1
16.7%
i 1
16.7%
u 1
16.7%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%
Uppercase Letter
ValueCountFrequency (%)
M 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21
75.0%
Latin 7
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 6
28.6%
5 4
19.0%
8 3
14.3%
. 3
14.3%
4 3
14.3%
2 1
 
4.8%
9 1
 
4.8%
Latin
ValueCountFrequency (%)
m 2
28.6%
M 1
14.3%
a 1
14.3%
x 1
14.3%
i 1
14.3%
u 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 6
21.4%
5 4
14.3%
8 3
10.7%
. 3
10.7%
4 3
10.7%
m 2
 
7.1%
M 1
 
3.6%
a 1
 
3.6%
x 1
 
3.6%
i 1
 
3.6%
Other values (3) 3
10.7%

Unnamed: 19
Categorical

MISSING 

Distinct2
Distinct (%)40.0%
Missing10640
Missing (%)> 99.9%
Memory size665.4 KiB
0
Minimum

Length

Max length7
Median length1
Mean length2.2
Min length1

Characters and Unicode

Total characters11
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)20.0%

Sample

1st rowMinimum
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4
 
< 0.1%
Minimum 1
 
< 0.1%
(Missing) 10640
> 99.9%

Length

2024-02-06T04:17:33.264196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-06T04:17:33.416424image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4
80.0%
minimum 1
 
20.0%

Most occurring characters

ValueCountFrequency (%)
0 4
36.4%
i 2
18.2%
m 2
18.2%
M 1
 
9.1%
n 1
 
9.1%
u 1
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6
54.5%
Decimal Number 4
36.4%
Uppercase Letter 1
 
9.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 2
33.3%
m 2
33.3%
n 1
16.7%
u 1
16.7%
Decimal Number
ValueCountFrequency (%)
0 4
100.0%
Uppercase Letter
ValueCountFrequency (%)
M 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7
63.6%
Common 4
36.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 2
28.6%
m 2
28.6%
M 1
14.3%
n 1
14.3%
u 1
14.3%
Common
ValueCountFrequency (%)
0 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4
36.4%
i 2
18.2%
m 2
18.2%
M 1
 
9.1%
n 1
 
9.1%
u 1
 
9.1%

Interactions

2024-02-06T04:17:26.281697image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:21.737812image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:22.678285image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:23.421081image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:24.168760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:24.913783image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:25.672462image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:26.404465image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:21.951876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:22.742404image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:23.573973image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:24.275967image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:24.981586image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:25.804270image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:26.521135image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:22.087837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:22.858955image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:23.679699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:24.401656image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:25.078689image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:25.868945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:26.585665image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:22.303260image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:23.013038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:23.848289image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:24.527702image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:25.246325image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:25.944182image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:26.658417image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:22.419016image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:23.094575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:23.936258image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:24.623081image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:25.369931image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:26.015680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:26.825516image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:22.516232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:23.179407image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:24.010531image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:24.760877image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:25.451952image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:26.095614image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:26.966924image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:22.616142image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:23.294122image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:24.081141image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:24.832785image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:25.544901image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T04:17:26.169489image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-02-06T04:17:27.113766image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-06T04:17:27.425365image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ProjectVersionClassWMCDITNOCCBOLCOMMAXCCAVGCCUnnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16Unnamed: 17Unnamed: 18Unnamed: 19
0jEdit4.2org.gjt.sp.jedit.gui.KeyEventWorkaround310718528.666666NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1jEdit4.3org.gjt.sp.jedit.gui.KeyEventWorkaround71010198525.142857NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2jEdit4.1org.gjt.sp.jedit.gui.KeyEventWorkaround410606220.500000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3jEdit4org.gjt.sp.jedit.gui.KeyEventWorkaround410606020.000000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4jEdit4.1org.gjt.sp.jedit.pluginmgr.PluginManager$ActionHandler2101503015.000000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
5jEdit3.2.1org.gjt.sp.jedit.browser.BrowserPopupMenu$ActionHandler2101402914.500000NaNNaNMAXCCNaNNaNNaNAVGCCNaNNaNNaN
6jEdit4.1org.gjt.sp.jedit.browser.BrowserCommandsMenu$ActionHandler210902914.500000NaNNaNAverageMedianMaximumMinimumAverageMedianMaximumMinimum
7jEdit4org.gjt.sp.jedit.pluginmgr.PluginManager$ActionHandler2101402512.500000NaNjEdit4.88173207216701.650023515128.6666660
8jEdit3.2.1org.gjt.sp.jedit.pluginmgr.PluginManager$ActionHandler2101402412.000000NaNcamel2.02237762213300.9151589430.857142879.50
9jEdit4.2org.gjt.sp.jedit.textarea.JEditTextArea$MouseHandler10301802211.100000NaNivy3.01500535912901.178112428180
ProjectVersionClassWMCDITNOCCBOLCOMMAXCCAVGCCUnnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16Unnamed: 17Unnamed: 18Unnamed: 19
10635jEdit4.3org.gjt.sp.jedit.options.SyntaxHiliteOptionPane$10102000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
10636jEdit4.3org.gjt.sp.jedit.Debug21027100.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
10637jEdit4.3org.gjt.sp.jedit.textarea.ExtensionManager$Entry1102000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
10638jEdit4.3org.gjt.sp.jedit.EditPane$CaretInfo2103100.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
10639jEdit4.3org.gjt.sp.jedit.gui.statusbar.SelectionLengthWidgetFactory$10102000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
10640jEdit4.3org.gjt.sp.jedit.gui.ToolBarManager$Entry1101000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
10641jEdit4.3org.gjt.sp.jedit.Abbrevs$Expansion1101000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
10642jEdit4.3org.gjt.sp.jedit.bsh.ClassPathException1403000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
10643jEdit4.3org.gjt.sp.jedit.msg.EditorExiting1203000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
10644jEdit4.3org.gjt.sp.jedit.gui.CloseDialog$10103000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN